Alternatives to Papertrail logo

Alternatives to Papertrail

Sentry, Splunk, Logstash, Loggly, and Logentries are the most popular alternatives and competitors to Papertrail.
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What is Papertrail and what are its top alternatives?

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.
Papertrail is a tool in the Log Management category of a tech stack.

Papertrail alternatives & related posts

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Johnny Bell
Johnny Bell
Senior Software Engineer at StackShare · | 7 upvotes · 215K views
Redux
Redux
Bugsnag
Bugsnag
Sentry
Sentry
LogRocket
LogRocket
JavaScript
JavaScript
React
React
#ErrorBoundry
#OpenSorce
#Chrome
#OpenSource

For my portfolio websites and my personal OpenSource projects I had started exclusively using React and JavaScript so I needed a way to track any errors that we're happening for my users that I didn't uncover during my personal UAT.

I had narrowed it down to two tools LogRocket and Sentry (I also tried Bugsnag but it did not make the final two). Before I get into this I want to say that both of these tools are amazing and whichever you choose will suit your needs well.

I firstly decided to go with LogRocket the fact that they had a recorded screen capture of what the user was doing when the bug happened was amazing... I could go back and rewatch what the user did to replicate that error, this was fantastic. It was also very easy to setup and get going. They had options for React and Redux.js so you can track all your Redux.js actions. I had a fairly large Redux.js store, this was ended up being a issue, it killed the processing power on my machine, Chrome ended up using 2-4gb of ram, so I quickly disabled the Redux.js option.

After using LogRocket for a month or so I decided to switch to Sentry. I noticed that Sentry was openSorce and everyone was talking about Sentry so I thought I may as well give it a test drive. Setting it up was so easy, I had everything up and running within seconds. It also gives you the option to wrap an errorBoundry in React so get more specific errors. The simplicity of Sentry was a breath of fresh air, it allowed me find the bug that was shown to the user and fix that very simply. The UI for Sentry is beautiful and just really clean to look at, and their emails are also just perfect.

I have decided to stick with Sentry for the long run, I tested pretty much all the JS error loggers and I find Sentry the best.

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Splunk logo

Splunk

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Search, monitor, analyze and visualize machine data
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    Grafana
    Grafana
    Splunk
    Splunk
    Kibana
    Kibana

    I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

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    Tymoteusz Paul
    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 12 upvotes · 196.6K views
    Amazon EC2
    Amazon EC2
    LXC
    LXC
    CircleCI
    CircleCI
    Docker
    Docker
    Git
    Git
    Vault
    Vault
    Apache Maven
    Apache Maven
    Slack
    Slack
    Jenkins
    Jenkins
    TeamCity
    TeamCity
    Logstash
    Logstash
    Kibana
    Kibana
    Elasticsearch
    Elasticsearch
    Ansible
    Ansible
    VirtualBox
    VirtualBox
    Vagrant
    Vagrant

    Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

    It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

    I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

    We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

    If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

    The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

    Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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    Tanya Bragin
    Tanya Bragin
    Product Lead, Observability at Elastic · | 10 upvotes · 34.6K views
    atElasticElastic
    Kibana
    Kibana
    Logstash
    Logstash
    Elasticsearch
    Elasticsearch

    ELK Stack (Elasticsearch, Logstash, Kibana) is widely known as the de facto way to centralize logs from operational systems. The assumption is that Elasticsearch (a "search engine") is a good place to put text-based logs for the purposes of free-text search. And indeed, simply searching text-based logs for the word "error" or filtering logs based on a set of a well-known tags is extremely powerful, and is often where most users start.

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    Logentries logo

    Logentries

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    Real-time log management and analytics built for the cloud
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    Logentries
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    #Heroku

    Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

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    related Datadog posts

    Robert Zuber
    Robert Zuber
    CTO at CircleCI · | 8 upvotes · 63.9K views
    atCircleCICircleCI
    Looker
    Looker
    PostgreSQL
    PostgreSQL
    Amplitude
    Amplitude
    Segment
    Segment
    Rollbar
    Rollbar
    Honeycomb
    Honeycomb
    PagerDuty
    PagerDuty
    Datadog
    Datadog

    Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

    We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

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    StackShare Editors
    StackShare Editors
    Flask
    Flask
    AWS EC2
    AWS EC2
    Celery
    Celery
    Datadog
    Datadog
    PagerDuty
    PagerDuty
    Airflow
    Airflow
    StatsD
    StatsD
    Grafana
    Grafana

    Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

    Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

    There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

    Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

    Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

    Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

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    Fluentd logo

    Fluentd

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    Unified logging layer
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    ELK logo

    ELK

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    The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
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      Wallace Alves
      Wallace Alves
      Cyber Security Analyst · | 1 upvotes · 13.3K views
      nginx
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      Elasticsearch
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      ELK
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      Portainer
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      Docker Compose
      Docker Compose
      Docker
      Docker

      Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

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      Sumo Logic logo

      Sumo Logic

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      Cloud Log Management for Application Logs and IT Log Data
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      Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

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      AWS CloudTrail logo

      AWS CloudTrail

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      Record AWS API calls for your account and have log files delivered to you
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      Splunk Cloud logo

      Splunk Cloud

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      Easy and fast way to analyze valuable machine data with the convenience of software as a service (SaaS)
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      #Heroku

      Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

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      Filebeat logo

      Filebeat

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      A lightweight shipper for forwarding and centralizing log data
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        logz.io

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        A log management and log analysis service
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          Scalyr logo

          Scalyr

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          SLF4J

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            Apache Flume logo

            Apache Flume

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            A service for collecting, aggregating, and moving large amounts of log data
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              Logback logo

              Logback

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              A logging framework for Java applications
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